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Waste Not, Want Not: Using Big Data to Improve Healthcare

In the United States today, healthcare is a hotly debated topic. While everyone wants a better system, there is a large amount of conflict regarding how to achieve improvement. Key among any approach to improve the system is a single question: how will we pay for it? As with many problems, funding seems as elusive as it is essential. What if through the power of big data, hundreds of billions of dollars were seemingly pulled out of thin air? The Institute of Medicine (IOM) contends we could do just that. According to an extensive report in 2013, the IOM estimated that in the US healthcare system alone $765 billion goes to waste each year [1]. For scale, the proposed total budget for national security in 2019 is $716 billion [2]. The extent of this wasted funding is hard to believe, but utilizing modern data collection and processing techniques it is possible to curtail this problem. Big data solutions can allow the healthcare industry to keep track of what is being wasted, reduce administrative excess, and improve care efficiency. One can only imagine the improvements that could be made in this sector if even a portion of the ill-spent funding could be reinvested in improving the quality of patient care, or funding research for novel cures.

Tracking What is Wasted

The first step in solving any problem is identifying the source. Tracking supplies ordered versus supplies used for an entire hospital sounds like a daunting task, but utilizing modern data processing techniques it can be achieved with relative ease. In most facilities orders, supply requests, billing invoices, etc. are already digitized.  Taking the next step to integrate all these records would allow for quick analysis of spending and utilization patterns. Once all the information is in a central location, it can be sorted and evaluated from a variety of perspectives including types of procedure, time of year, and even down to individual personnel.  Strategic and incremental implementation of oversight practices can also break the enormous task into more manageable pieces that can be overseen by individual departments. At UCSF, reviewing the lists of supplies surgeons prepped for an operation resulted in identifying many items that were unnecessary and were eliminated from lists in the future [3]. Automating this process using an algorithm that cross-references materials requested against those used could give monthly or even weekly recommendations of supplies to conserve. This could also automatically generate recommendations for future supply orders, one of several administrative tasks that big data can assist with.

Figure 1 Sources of waste and excess costs in US healthcare [1]

Reducing Administrative Excess

According to the IOM breakdown of wasted money in health care [Figure 1], a significant portion is attributed to excess administrative costs. In 2014, a comparison of hospitals in eight countries including found that hospitals in the United States used 25.3% of their overall spending for administrative costs – more than any of the other countries considered [4]. While some of that cost is necessary for facility function, there is certainly a portion that is superfluous. None of the other countries studied spent more than 20% of their overall budget, with our Canadian neighbors to the north spending a mere 12.4% [Figure 2]. Many administrative tasks can be streamlined with the use of big data. Because of the variety and complexity of billing and insurance related activities in the US healthcare system today, there are many areas of redundancy and inefficiency [5]. Getting payment from insurers often requires dealing with details and circumstances that are unique to individual patients. As a result, when an employee spends time to solve a specific problem, that knowledge is not easily passed on and other employees must spend even more time repeating the work that has already been done in order to reach the same result. A central database that can reference claims based on payer and billing item would easily make this process more efficient. Additionally, if multiple claims for a payer are rejected for the same reason, the administrative staff can alter their practices accordingly to avoid delay and redundant labor in the future.

Figure 2 Percentage of funds spent on administrative costs for the US compared to other countries [4]

Better Quality of Care

There are few industries in our world that are working at peak efficiency, and the field of medicine is no exception. In Figure 1, we see that unnecessary services, inefficient care delivery, and missed prevention opportunities account for over 50% of losses. Centralized electronic health records that can be easily accessed and compared by physicians would immediately impact these areas. Doctors could compare how different populations with a disease responded to different treatments in order to choose the best course of action for a current patient.

Computerized physician order entry (CPOE) systems are gaining traction in the industry and have produced significant results. The CPOE system, used when a doctor orders a prescription, highlights patient allergies to prescriptions and identifies potential interactions between medications already in use. Studies have found that the use of this system resulted in anywhere from a 41-81% reduction in overall medication errors [1]. If using static information like this has such a demonstrable effect on improving treatment, dynamic information that increases in breadth and accuracy with each patient has phenomenal potential.  Monitoring trends in any number of hospital metrics can lead to many small improvements that add up to huge savings.

Big data engines such as Google Analytics are able to produce mountains of specific information on potential customers to allow companies to target and market to them more effectively. Why has this mindset not been applied to healthcare, arguably the most far reaching industry in our country? Amazon holds a patent for a system of ‘anticipatory shipping’ that allows them to stock local warehouses with goods they predict will be purchased in the area based on previously gathered data [6]. If Amazon can confidently predict the sales of a specific item in a particular population, there is no reason medical professionals should be lacking the technology to predict disease likelihoods in a similar group.

If patient metrics could be used for demographic disease analysis in real time, hospitals could implement preventative measures specific to their community. Curtailing the progression of chronic conditions saves care costs for the entire lifetime of a patient, but prevention intervention is more likely to be effective if tailored to the culture and unique dynamics of a population [7]. More acutely, if a contagious illness with generic initial symptoms begins spreading through an area and clinicians across multiple facilities are immediately aware of an increasing number of confirmed cases, fewer future cases are likely to be dismissed and allowed to worsen. A missed diagnosis is not only an inefficient use of physician time, but it also generally means the condition progresses and becomes costlier to treat. It is estimated that if medical facilities were running at maximum efficacy, 75,000 fewer deaths would have occurred nationwide in 2005 – approximately 1 person every 7 minutes [1]. If the unnecessary cost to hospitals is not enough of a motivation for change, the unnecessary cost of human life certainly should be.

Big data has long been regarded as a silver bullet for improving everything from supply chain management to self-driving cars. While these applications are exciting, applying this technology to healthcare could truly mean the difference between life and death. Using big data to eliminate wasteful spending and excess costs in medicine is an obvious and much needed solution to improving this field. Not only can big data be used to identify sources of these inefficiencies, but it can also be used to implement improvements. At Rock West we know that while not all industries may be as massive as healthcare, they do all face great challenges to improve efficiency. We want to help you identify and design solutions to overcome those hurdles. Contact us today to see if we can help your business reach its maximum potential.

Sources:

  1. Institute of Medicine. 2013. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington, DC: The National Academies Press. https://doi.org/10.17226/13444.
  2. https://dod.defense.gov/News/News-Releases/News-Release-View/Article/1438798/dod-releases-fiscal-year-2019-budget-proposal/
  3. https://www.npr.org/sections/health-shots/2017/12/21/572329335/a-prescription-to-reduce-waste-in-health-care-spending
  4. D. U. Himmelstein, M. Jun, R. Busse et al., “A Comparison of Hospital Administrative Costs in Eight Nations: U.S. Costs Exceed All Others by Far,” Health Affairs, Sept. 2014 33(9):1586–94. https://www.commonwealthfund.org/publications/journal-article/2014/sep/comparison-hospital-administrative-costs-eight-nations-us
  5. Institute of Medicine (US) Roundtable on Evidence-Based Medicine; Yong PL, Saunders RS, Olsen LA, editors. https://www.ncbi.nlm.nih.gov/books/NBK53942/
  6. https://www.forbes.com/sites/onmarketing/2014/01/28/why-amazons-anticipatory-shipping-is-pure-genius/#382b47294605
  7. Lagisetty, P. A., Priyadarshini, S., Terrell, S., Hamati, M., Landgraf, J., Chopra, V., & Heisler, M. (2017). Culturally Targeted Strategies for Diabetes Prevention in Minority Population. The Diabetes educator43(1), 54-77.